Subspecific variation in gut microbiota of North American bison in a sympatric setting reveals differentially abundant taxa
Bibliographic record
Abstract
Gut microbiomes play critical roles in host-environment interactions, reflecting habitat and foraging niches. North American bison (Bison bison) subspecies—plains bison (B. bison bison) and wood bison (B. bison athabascae)—exhibit limited genetic variation from historic population bottleneck events, potentially undermining their evolutionary potential. Understanding variation in gut microbiota composition between subspecies may shed light on genetic, phenotypic, and ecological divergence relevant to their adaptive capacities. Using 16S rRNA metabarcoding of fecal samples, we characterized the gut microbiota of both subspecies in the sympatric environment of Elk Island National Park, providing insight into potential phylogenetic gut microbiome divergence. Like other ruminants, the gut microbial community of both subspecies consists primarily of the bacterial phyla Firmicutes and Bacteroidetes. Subspecific classification explained no significant differences in alpha diversity (p > 0.05) in the overall dataset, but has a potentially significant effect on beta diversity (p < 0.05, R2 = 0.04). Gut microbiota divergence between subspecies may be driven by differential abundance of specific taxa and associated functional pathways, likely influenced by dietary preferences, ancestral phenotypes, and historical ranges. Our findings support further investigation into diet-microbiome relationships between subspecies in sympatric environments and metagenomic approaches to explore functional differences in the gut microbiome.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".